Challenges in the Clinical Recognition of Acute Flaccid Myelitis and its Implications
Bibliographic record
Abstract
OBJECTIVES: To explore the challenges in diagnosing acute flaccid myelitis (AFM) and evaluate clinical features and treatment paradigms associated with under recognition. STUDY DESIGN: This was a retrospective multicenter study of pediatric patients (≤18 years) who were diagnosed with AFM from 2014 to 2018 using the Centers for Disease Control and Prevention's case definition. RESULTS: In 72% of the cases (126 of 175), AFM was not considered in the initial differential diagnosis (n = 108; 61.7%) and/or the patient was not referred for acute care (n = 90; 51.4%) at the initial clinical encounter, and this did not improve over time. Although many features of the presentation were similar in those initially diagnosed with AFM and those who were not; preceding illness, constipation, and reflexes differed significantly between the 2 groups. Patients with a non-AFM initial diagnosis more often required ventilatory support (26.2% vs 12.2%; OR, 0.4; 95% CI, 0.2-1.0; P = .05). These patients received immunomodulatory treatment later (3 days vs 2 days after neurologic symptom onset; 95% CI, -2 to 0; P = .05), particularly intravenous immunoglobulin (5 days vs 2 days; 95% CI, -4 to -2; P < .001). CONCLUSIONS: Delayed recognition of AFM is concerning because of the risk for respiratory decompensation and need for intensive care monitoring. A non-AFM initial diagnosis was associated with delayed treatment that could have a clinical impact, particularly as new treatment options emerge.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".